The disclosure relates generally to power system control and operation, and more particularly to control and operation of a power system including an electrolyzer that produces hydrogen gas via electrolysis.
In power systems, particularly in so-called “smart grid” power systems employing renewable energy sources, it is often desirable to store excess energy for use during times when power demand exceeds power generation capacity. For example, in a system employing wind, solar, and hydroelectric power generation, an excess of energy may be produced on a clear, windy day, but on a cloudy, calm day, or a calm night, power demand may exceed what these sources may produce. Various solutions have been realized, with using hydrogen gas as an energy carrier being particularly attractive due to its relatively high heating value and its storage. Hydrogen may be used by producing it using excess electricity, such as by electrolysis.
Electrolysis is one of the well-established technologies for hydrogen production. Electricity is used by an electrolyzer to generate hydrogen with oxygen and heat as byproducts. The generated hydrogen is then compressed and stored in, for example, tube trailers which can be used by a fuel cell plant to generate electricity at any time. This approach is particularly of interest in small isolated or grid-tied microgrids.
Electrolyzer start-up and shut-down of the electrolyzer, however, require some time to avoid damage due to shifts of temperature and pressure. In addition, an electrolyzer typically should produce a minimum hydrogen production to function. Therefore, in order to increase the life cycle, physical integrity and higher performance of the electrolyzer, some operational constraints such as minimum up-time, down-time and charging power are defined.
In power generation optimization problems generally (e.g., optimal dispatch within a microgrid where an electrolyzer is included), the aforementioned operational constraints should be considered. If power generation optimization is formulated in the form of mixed integer nonlinear programming (MINLP) or mixed integer linear programming (MILP) problems, operational constraints that are typically complex may be considered, but require substantial computing resources and time. In fact, MINLP and MILP analyses present such computational challenges that the use of these techniques is impractical for real-time and fast-response applications. Conventional linear programming (LP) is a practical technique, but is not suitable since LP cannot consider the above-mentioned, complex operational constraints.